Information-driven transitions in projections of underdamped dynamics

Giorgio Nicoletti, Amos Maritan, Daniel M. Busiello

Published in Phys. Rev. E 106, 014118 (2022), 2022

Recommended citation: Giorgio Nicoletti, Amos Maritan, Daniel M. Busiello. Information-driven transitions in projections of underdamped dynamics. Phys. Rev. E 106, 014118 (2022).

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Abstract

Low-dimensional representations of underdamped systems often provide useful insights and analytical tractability. Here, we build such representations via information projections, obtaining an optimal model that captures the most information on observed spatial trajectories. We show that, in paradigmatic systems, the minimization of the information loss drives the appearance of a discontinuous transition in the optimal model parameters. Our results raise serious warnings for general inference approaches, and they unravel fundamental properties of effective dynamical representations impacting several fields, from biophysics to dimensionality reduction.